Operations, such as geophysical surveying, drilling, logging, well completion, and production, are typically performed to locate and gather valuable downhole fluids. Surveys are often performed using acquisition methodologies, such as seismic mapping and resistivity mapping, to generate images of underground formations. These formations are often analyzed to determine the presence of subterranean assets, such as valuable fluids or minerals, or to determine if the formations have characteristics suitable for storing fluids. Although the subterranean assets are not limited to hydrocarbons such as oil, throughout this document, the terms “oilfield” and “oilfield operation” may be used interchangeably with the terms “field” and “field operation” to refer to a site where any types of valuable fluids or minerals can be found and the activities to extract them. The terms may also refer to sites where substances are deposited or stored by injecting them into the subsurface using boreholes and the operations associated with this process. Further, the term “field operation” refers to a field operation associated with a field, including activities related to field planning, wellbore drilling, wellbore completion, and/or production using the wellbore.
Simulations are commonly used in the oil industry and other industries to model processes and predict behaviors. Each type of simulation is relevant to a certain scale of process. A common example in the oil industry is the use of reservoir flow models to predict dynamic behavior at the scale of a reservoir, which can be from a few meters to hundreds of meters thick and can be thousands of meters in lateral extent. The volume elements in these models are typically on the order of meters or tens of meters on a side. Reservoir scale processes, such as developed miscibility, can develop within the model.
At the other extreme, micromodels of porous media represent small pieces of the media, typically with volume elements on the order of a few microns for micro-computed tomography (micro-CT) or less (e.g., 100 times smaller for scanning electron microscopy (SEM) imaging) on a side and full models that are on the order of millimeters or less in extent. In these models, the small size means the residence time of fluids within the model is too short for many processes to develop fully. The present disclosure is within the domain of these small models.
Static micromodels representing pore and grain geometry can be obtained in several ways at different scales. Thin sections of rocks are formed by injecting a colored epoxy into a rock and then slicing an optically thin section and mounting it onto a glass slide. This is optically analyzed to obtain images of the pores and grains. Multiple thin sections can be used to create a micromodel, typically using statistical distributions rather than making an image directly from stacked thin sections. Alternatively, a small rock volume can be scanned using X-rays in a micro-CT machine. The tomographic inversion of the X-ray scans is used to create a static model of a rock with resolution ranging from tens of microns to tens of nanometers. This computed tomography (CT) image is processed and segmented into grains and pores. A third method uses ion beam milling and scanning electron microscopy to create a series of images with nanometer-scale resolution. These images can be analyzed and used to construct a static three-dimensional (3D) model of a tiny portion of the rock.
Micromodels for flow-dynamic behavior in porous media are of a few types. Pore network models substitute a complex network of nodes and connectors to represent the pores and pore throats, respectively. The network is based on a static representation rock model, and flow dynamics are applied to the pore network. Lattice Boltzmann models are based on the movement of particles on 3D grid, which can be placed within a static rock model. A third method uses microhydrodynamical modeling in a static rock model to represent simple or complex fluid-fluid and fluid-rock interactions during flow or while a chemical process develops.
All of these micromodels represent small portions of a real rock. Processes that require time, distance, or extensive gradients, for example of pressure or concentration, are not captured in such a simulation.
History matching is a procedure for reservoir modeling. Inputs to the model include seismic data, well logs, core description and core data, production rates and pressures, and well tests, as examples. From these data, a model of the reservoir and its flow characteristics is developed. A flow simulation results in predictions of well production rates and pressures at wells, among other data. Discrepancies between the predicted and measured well data indicate places that the reservoir model may be adjusted, after which the model is rerun and evaluated again in comparison with the measured data.